This paper studies the most popular cryptocurrency, Bitcoin, which is currently traded on more than 500 exchanges. Since Bitcoin is the first cryptocurrency, established in 2008 [1]}, it provides the longest historical data to study. Compared to traditional financial instruments like equities and commodities, cryptocurrencies like Bitcoin have large, so-called `whale' holders, which consist of about 1,000 people who own around 40% of the market [2]}. In this paper, we explore how large Bitcoin transactions from these whales affect the market volatility. We propose a state-of-the-art deep learning Synthesizer Transformer model [3]} that predicts if Bitcoin's volatility will be extreme the next day, based on transaction data from these whales as well as a variety of features from CryptoQuant, including on-chain metrics, miner flows, and more. We compare this proposed model with existing baseline models and propose a simple trading strategy to demonstrate the practical usefulness of the predictions. In our experiments, we also analyse the importance of the different CryptoQuant and whale-alert features that most influence volatility. An overview of our paper is provided in Figure REF . The code of our proposed (trained) models is made available onlinehttps://github.com/dorienh/bitcoin_synthesizer.
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